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Semiring programming: A semantic framework for generalized sum product problems.
- Source :
-
International Journal of Approximate Reasoning . Nov2020, Vol. 126, p181-201. 21p. - Publication Year :
- 2020
-
Abstract
- To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving real-world problems requires an integration amongst these, contemporary representation methodologies offer little support for this. In an attempt to alleviate this situation, we position and motivate a new declarative programming framework in this paper. We focus on the semantical foundations in service of providing abstractions of well-known problems such as SAT, Bayesian inference, generative models, learning and convex optimization. Programs are understood in terms of first-order logic structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines and represent non-standard problems over unbounded domains. Thus, the main thrust of this paper is to view such well-known problems through a unified lens in the hope that appropriate solver strategies (exact, approximate, portfolio or hybrid) may emerge that tackle real-world problems in a principled way. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0888613X
- Volume :
- 126
- Database :
- Academic Search Index
- Journal :
- International Journal of Approximate Reasoning
- Publication Type :
- Periodical
- Accession number :
- 146194551
- Full Text :
- https://doi.org/10.1016/j.ijar.2020.08.001